Bringing proportional recovery into proportion: Bayesian modelling of post-stroke motor impairment
- Submitting institution
-
The University of Kent
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 20148
- Type
- D - Journal article
- DOI
-
10.1093/brain/awaa146
- Title of journal
- Brain
- Article number
- -
- First page
- 2189
- Volume
- 143
- Issue
- 7
- ISSN
- 0006-8950
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2020
- URL
-
https://kar.kent.ac.uk/81216/
- Supplementary information
-
-
- Request cross-referral to
- 4 - Psychology, Psychiatry and Neuroscience
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
9
- Research group(s)
-
-
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- In England, one in six people will have a stroke in their lifetime. Consequently, accurately predicting a patient’s recovery trajectory following stroke is critical. A central theory was that patients recover in proportion to the behavioural impairment arising from a stroke. This paper is significant because, using Bayesian model recovery simulations, we show that the proportional recovery hypothesis is confounded, and has much less explanatory value than previously believed. The findings in this paper prompt a re-evaluation of how stroke patients recover, implying that recovery cannot be accurately predicted just from behavioural impairment, indicating the importance of neuroimaging methods.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
